Deep Multi-Task Model for Sarcasm Detection and Sentiment Analysis in Arabic Language. (arXiv:2106.12488v1 [cs.CL])
Abdelkader El Mahdaouy, Abdellah El Mekki, Kabil Essefar, Nabil El Mamoun, Ismail Berrada, Ahmed Khoumsi
The prominence of figurative language devices, such as sarcasm and irony,
poses serious challenges for Arabic Sentiment Analysis (SA). While previous
research works tackle SA and sarcasm detection separately, this paper
introduces an end-to-end deep Multi-Task Learning (MTL) model, allowing
knowledge interaction between the two tasks. Our MTL model's architecture
consists of a Bidirectional Encoder Representation from Transformers (BERT)
model, a multi-task attention interaction module, and two task classifiers. The
overall obtained results show that our proposed model outperforms its
single-task counterparts on both SA and sarcasm detection sub-tasks.